Support quality, written down.

Your AI agent closes tickets alone and its worst answers never escalate. How to find out if it is doing damage: what to sample first, and what to score.

Rules for writing QA criteria a machine can score consistently, because most AI scoring complaints are rubric problems rather than model problems.

High QA scores and unhappy customers usually means one of five things. Check whether the two numbers even describe the same conversations first.

A total score cannot be disputed. See what every AI QA deduction must carry: the criterion, the quoted moment, the rule version, and who can overturn it.

Copy a ready-made scorecard for support, live chat, email, voice or complaints, with plain-English definitions and suggested weights, no sign-up needed.

QA tool rollouts fail on people, not configuration. A week by week plan: what to tell agents first, which team to pilot, and why month one scores drop.

Your team grew and QA quietly stopped working. What breaks at 10, 30 and 100 agents, the tell for each one, and the single thing to change at each threshold.

Quality assurance on Salesforce Service Cloud cases: what to evaluate, how case data maps to scorecard criteria, and how it differs from scoring Zendesk.

System errors, customers who hang up and silent escalations should not cost an agent points. How to find control-dependent criteria and fix them properly.

Run automated QA scoring alongside your manual reviews, hidden from agents, and compare. How long to shadow, what to measure, and the exit test to go live.

A support lead, a VP and a CFO need three different reports, not one. How to translate support quality into cost, risk and retention, and defend the number.

Most team leads coach the score instead of the cause. A weekly method for reading QA results, choosing who to coach, and knowing when to coach nobody at all.